Skip to main content
Log in

Detection of surface cutting defect on magnet using Fourier image reconstruction

  • Mechanical Engineering, Control Science and Information Engineering
  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

A magnet is an important component of a speaker, as it makes the coil move back forth, and it is commonly used in mobile information terminals. Defects may appear on the surface of the magnet while cutting it into smaller slices, and hence, automatic detection of surface cutting defect detection becomes an important task for magnet production. In this work, an image-based detection system for magnet surface defect was constructed, a Fourier image reconstruction based on the magnet surface image processing method was proposed. The Fourier transform was used to get the spectrum image of the magnet image, and the defect was shown as a bright line in it. The Hough transform was used to detect the angle of the bright line, and this line was removed to eliminate the defect from the original gray image; then the inverse Fourier transform was applied to get the background gray image. The defect region was obtained by evaluating the gray-level differences between the original image and the background gray image. Further, the effects of several parameters in this method were studied and the optimized values were obtained. Experiment results show that the proposed method can detect surface cutting defects in a magnet automatically and efficiently.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. XIE X. A review of recent advances in surface defect detection using texture analysis techniques [J]. Electronic Letters on Computer Vision and Image Analysis, 2008, 7: 1–22.

    Google Scholar 

  2. CONNERS R W, CHARLES W M, LIN K, RAMON E V E. Identifying and locating surface defects in wood: Part of an automated lumber processing system [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5: 573–583.

    Article  Google Scholar 

  3. PERNKOPF F. Detection of surface defects on raw steel blocks using Bayesian network classifiers [J]. Pattern Anal Applic, 2004, 7: 333–342.

    Article  MathSciNet  Google Scholar 

  4. SHANKAR N G, ZHONG Z W. Defect detection on semiconductor wafer surfaces [J]. Microelectronic Engineering, 2005, 77: 337–346.

    Article  Google Scholar 

  5. BASHKANSKY M, DUNCAN M D, KAHN M. Subsurface defect detection in ceramics by high-speed high-resolution optical coherent tomography [J]. Optics Letters, 1997, 22: 61–63.

    Article  Google Scholar 

  6. DAVENEL A, GUIZARD C H, LABARREZ T. Automatic detection of surface defects on fruit by using a vision system [J]. Journal of Agricultural Engineering Research, 1988, 41: 1–9.

    Article  Google Scholar 

  7. GUNATILAKE P, SIEGEL M, JORDAN A G. Image understanding algorithms for remote visual inspection of aircraft surfaces [C]// Electronic Imaging'97—International Society for Optics and Photonics. New York, USA: IEEE Press, 1997: 2–13.

    Google Scholar 

  8. BHARATI M H, LIU J J, MACGREGOR J F. Image texture analysis: methods and comparisons [J]. Chemometrics and Intelligent Laboratory Systems, 2004, 72: 57–71.

    Article  Google Scholar 

  9. SOH L K, TSATSOULIS C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37: 780–795.

    Article  Google Scholar 

  10. CHAI H Y, WEE L K, SWEE T T, SALLEH S H, ARIFF A. Gray-level co-occurrence matrix bone fracture detection [J]. American Journal of Applied Sciences, 2011, 8: 26–32.

    Article  Google Scholar 

  11. SU H, WANG Y, XIAO J, LI L. Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 85: 13–20.

    Article  Google Scholar 

  12. MANIVANNAN K, AGGARWAL P, DEVABHAKTUNI V, KUMAR A, NIMS D, BHATTACHARYA P. Particulate matter characterization by gray level co-occurrence matrix based support vector machines [J]. Journal of Hazardous Materials, 2012, 223: 94–103.

    Article  Google Scholar 

  13. ZHANG D, ZHAO M, ZHOU Z, PAN S. Characterization of wire rope defects with gray level co-occurrence matrix of magnetic flux leakage images [J]. Journal of Nondestructive Evaluation, 2013, 32(1): 37–43.

    Article  Google Scholar 

  14. VENKAT RAMANA K, RAMAMOORTHY B. Statistical methods to compare the texture features of machined surfaces [J]. Pattern Recognition, 1996, 29: 1447–1459.

    Article  Google Scholar 

  15. IIVARINEN J, HEIKKINEN K, RAUHAMAA J, VUORIMAA P, VISA A. A defect detection scheme for web surface inspection [J]. International Journal of Pattern Recognition and Artificial Intelligence, 2000, 14: 735–755.

    Google Scholar 

  16. DING M, HUANG W, LI B, WU S, WEI Z, WANG Y. An automated cotton contamination detection system based on co-occurrence matrix contrast information [C]// Intelligent Computing and Intelligent Systems, 2009. New York, USA: IEEE Press, 2009: 517–521.

    Google Scholar 

  17. PASCHOS G. Fast color texture recognition using chromaticity moments [J]. Pattern Recognition Letters, 2000, 21: 837–841.

    Article  Google Scholar 

  18. WILTSCHI K, PINZ A, LINDEBERG T. Automatic assessment scheme for steel quality inspection [J]. Machine Vision and Applications, 2000, 12: 113–128.

    Article  Google Scholar 

  19. KUMAR A, PANG G K. Defect detection in textured materials using Gabor filters [C]// IEEE Transactions on Industry Applications, 2002, 38: 425–440.

    Article  Google Scholar 

  20. BODNAROVA A, BENNAMOUN M, LATHAM S. Optimal gabor filters for textile flaw detection [J]. Pattern Recognition, 2002, 35: 2973–2991.

    Article  MATH  Google Scholar 

  21. MALLAT S G. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11: 674–693.

    Article  MATH  Google Scholar 

  22. CHEN T, KUO C C J. Texture analysis and classification with tree-structured wavelet transform [J]. IEEE Transactions on Image Processing, 1993, 2: 429–441.

    Article  Google Scholar 

  23. MARUO K, SHIBATA T, YAMAGUCHI T, ICHIKAWA M, OHMI T. Automatic defect pattern detection on LSI wafers using image processing techniques [J]. IEICE Transactions on Electronics, 1999, 6: 1003–1012.

    Google Scholar 

  24. SCHARCANSKI J. Stochastic texture analysis for monitoring stochastic processes in industry [J]. Pattern Recognition Letters, 2005, 26: 1701–1709.

    Article  Google Scholar 

  25. YANG X, PANG G, YUNG N. Robust fabric defect detection and classification using multiple adaptive wavelets [J]. IEE Proceedings Vision, Image Processing, 2005, 152(6): 715–723.

    Article  Google Scholar 

  26. LIU S S, JERNIGAN M E. Texture analysis and discrimination in additive noise [J]. Computer Vision, Graphics Image Process, 1990, 49: 52–67.

    Article  Google Scholar 

  27. CHAN C H, PANG G K H. Fabric defect detection by Fourier analysis [J]. IEEE Transactions on Industry Applications, 2000, 36: 1267–1276.

    Article  Google Scholar 

  28. KUMAR A. Computer-vision-based fabric defect detection: A survey [J]. IEEE Transactions on Industrial Electronics, 2008, 55: 348–363.

    Article  Google Scholar 

  29. OHSHIGE T, TANAKA H, MIYAZAKI Y, KANDA T, ICHIMURA H, KOSAKA N, TOMODA T. Defect inspection system for patterned wafers based on the spatial-frequency filterinaz [C]// IEEE/CHMT Int Electronic Manuf Technol Symp. San Francisco, CA, USA, 1991: 192–196.

    Google Scholar 

  30. TSAI D, HEISH C. Automated surface inspection for directional textures [J]. Image and Vision Computing, 1999, 18: 49–62.

    Article  Google Scholar 

  31. TSAI D M, WU S C, LI W C. Defect detection of solar cells in electroluminescence images using Fourier image reconstruction [J]. Solar Energy Materials and Solar Cells, 2012, 99: 250–262.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fu-liang Wang  (王福亮).

Additional information

Foundation item: Project(51575542) supported by the National Natural Science Foundation of China; Project(2016CX010) supported by the Innovation-Driven Project of CSU, China; Project(2015CB057202) supported by the National Basic Research Program of China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Fl., Zuo, B. Detection of surface cutting defect on magnet using Fourier image reconstruction. J. Cent. South Univ. 23, 1123–1131 (2016). https://doi.org/10.1007/s11771-016-0362-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-016-0362-y

Key words

Navigation